System and method to validate consistency of component business model maps

ABSTRACT

A system and method is described for using descriptive logic (DL) representations to validate consistency in component business model (CBM) maps. Semantic constraints are generated from a semantic model of a component business model meta-model and inconsistency conditions of CBM maps. The semantic model of the CBM meta-model is applied to transform CBM maps into corresponding semantic representations. An inference engine applies the semantic constraints to the semantic representations to determine inconsistencies between one CBM map and another and between a CBM map and the component business model meta-model.

This invention is related to commonly owned U.S. patent application Ser.No. 11/176,371 for “SYSTEM AND METHOD FOR ALIGNMENT OF AN ENTERPRISE TOA COMPONENT BUSINESS MODEL” which is incorporated by reference herein.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention generally relates to component based businessmodels and, more particularly, to a system and method for deducing andresolving potential inconsistencies in the semantic representation ofcomponent business model maps.

2. Background Description

Component Business Modeling is a state-of-art technology for modelingthe entire enterprise from a business perspective, driving informationtechnology (IT) solutions to help transform an enterprise from a currentAS-IS condition to a desired TO-BE condition. The component businessmodel (CBM) map is the key component in CBM methodology and CBM relatedtools. Component business modeling is a technique for modelingbusinesses based on a number of non-overlapping “business components,”which are defined as relatively independent collections of businessactivities. It provides simple business views for analysis, unliketraditional business process-based models which provide transactionalviews of businesses. The CBM methodology facilitates qualitativeanalysis techniques such as the dependency analysis (to identify “hot”components associated with business pain points), the heat map analysis(also to identify “hot” components associated with business painpoints), and the overlay analysis (to identify IT shortfalls of the“hot” components).

The CBM-based qualitative business analysis has been mostly conductedmanually by business consultants. What is needed for automation of theCBM-based business analyses is a semantic representation of thecomponent business model. In particular, there is a need to validate theCBM models by detecting inconsistencies 1) among the various CBM maps(that is, the universal CBM map at the broadest level, the intermediatelevel industry CBM maps, and the CBM maps for particular enterprises)and 2) between the CBM meta-model and a CBM map.

Some examples of inconsistency are as follows. Suppose the demandforecast and analysis component belongs to the marketing competency inthe CBM map for the retail industry. But a consultant working on a CBMmap for an enterprise within the retail industry may assign the demandforecast and analysis component to a different competency, say,financial management. Then the enterprise map is inconsistent with theretail industry map, but the consultant has no systematic methodologyfor identifying this kind of inconsistency.

Another simple example would be the cardinality inconsistency. Forinstance, the CBM meta-model specifies that a component has one and onlyone accountability level. When working on a CBM map, a consultant maygive a component more than one accountability level. This is not correctand will complicate further analysis, but the consultant may not beaware of the inconsistency because of the large number of components,activities or services in one CBM map.

Inconsistencies in CBM maps will set traps that will compromise theefficiency of further CBM related consulting. The manual validation ofCBM models and maps in order to avoid these inconsistencies is a tediousand error-prone process, causing significant degradation of productivityand accuracy of the CBM-based analysis. Therefore, some methods or toolsshould be developed to detect those inconsistencies as early aspossible.

SUMMARY OF THE INVENTION

One aspect of the invention is a method for automating the validation ofconsistency of component business models, comprising generating semanticconstraints from i) a semantic model of a component business modelmeta-model and ii) inconsistency conditions applicable to at least onecomponent business model (CBM) maps, using the semantic model totransform the CBM maps into corresponding semantic representations, andapplying the semantic constraints to the semantic representations todetermine inconsistencies between the component business modelmeta-model and one of the CBM maps. In this aspect of the invention itis preferred that the component business model be comprised ofnon-overlapping components arranged by accountability level withinnon-overlapping managing concepts, wherein one of the CBM maps is a mapof a business enterprise, and one of the CBM maps is a map of theindustry in which the business enterprise operates.

In another aspect, the method of the invention applies the semanticconstraints to the semantic representations to determine inconsistenciesbetween the enterprise CBM map and the industry CBM map. A furtheraspect of the invention's method is using a CBM tool to modify the CBMenterprise map to remove the inconsistencies. It is also an aspect ofthe invention to provide a service for validating the consistency of CBMmaps by using the method of the invention.

It is also an aspect of the invention to provide a system for automatingthe validation of consistency of component business models, the systembeing comprised of a component business model representation of abusiness enterprise, further comprising a component business modelmeta-model and at least one CBM maps of non-overlapping componentsarranged by accountability level within non-overlapping managingconcepts, one of the at least one CBM maps being a map of the businessenterprise, the system further being comprised of a generator forgenerating semantic constraints from i) a semantic model of thecomponent business model meta-model and ii) inconsistency conditionsapplicable to the at least one CBM maps, the system further beingcomprised of a transformer for using the semantic model to transform theCBM maps into corresponding semantic representations, the system furtherbeing comprised of an inference engine for applying the semanticconstraints to the semantic representations to determine inconsistenciesbetween the CBM meta-model and one of the CBM maps.

In another of its aspects, the system of the invention provides that thesemantic representation of the industry CBM map is a first ABox and thesemantic representation of the enterprise CBM map is a second ABox, andthe inference engine determines inconsistencies by performing aself-consistency validation on a third ABox, the third ABox being acombination of the first ABox and the second Abox. In a further aspectof the invention the component business model meta-model is a TBox, andthe inference engine determines inconsistencies by verifying whether thefirst ABox complies with the TBox and whether the second ABox complieswith the TBox. In yet another aspect, one of the CBM maps is a universalCBM map, and the semantic representation of the universal CBM map is afourth ABox A4. It is also an aspect of the system of the invention forthe inference engine to determine inconsistencies between the enterprisemap and the industry map and the universal map by performing aself-consistency validation of a fifth ABox A5, the fifth ABox being thecombination A1+A2+A4. A further aspect of the invention is a computerimplementation in computer code of the elements of the system.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, aspects and advantages will be betterunderstood from the following detailed description of a preferredembodiment of the invention with reference to the drawings, in which:

FIG. 1 is a flow chart showing how the overall logic of theinconsistency detection process.

FIG. 1A is a schematic of a system implementing the invention describedin FIG. 1.

FIG. 2A is a diagram showing a semantic model of a fragment of a CBMmeta-model. FIG. 2B is a diagram showing a mapping between the semanticmodel fragment of FIG. 2A and a representation of the fragment using theEcore meta-model.

FIG. 3 is a diagram showing a meta-model representation of a“hasCompetency” definition.

FIG. 4 is a diagram showing a CBM meta-model representation of a portionof a CBM retail industry map.

FIG. 5 is a diagram showing a graphical representation in OWL of aportion of a CBM map for a particular enterprise within the retailindustry, corresponding to FIG. 4.

FIG. 6 is a diagram showing a graphical representation in OWL ofsemantic constraints on cardinality.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT OF THE INVENTION

It is therefore a feature of the present invention to provide systematicidentification of inconsistencies in CBM maps.

Another feature of the invention is automation of identification ofinconsistencies in CBM maps.

A further feature of the invention is to provide a method of validatingconsistency of CBM maps.

It is also a feature of the invention to provide tools for earlydetection of inconsistencies in CBM maps.

The present invention is a novel approach to detecting inconsistenciesin CBM maps based on semantic technologies. The invention provides asemantic business model that uses a semantic markup language to describethe CBM maps and the CBM meta-model. For the purpose of illustrating theinvention the OWL markup language is used, but one skilled in the artwill appreciate that the same methodology can be used in other semanticmarkup languages such as W3C and RDF.

Analysis of CBM maps using the semantic business model discoversimplicit facts in the analyses by using inference capabilities ofontology by capturing relationships of relevant concepts such asbusiness components, business processes, business activities,operational metrics, performance indicators, value drivers, ITapplications, IT capabilities (systems, services, solutions, and thelike), and resources including human resources. A detailed structure ofthe semantic business model is captured in the CBM meta-model.

The invention uses the Component Business Model (CBM) described inrelated patent application Ser. No. 11/176,371 for “SYSTEM AND METHODFOR ALIGNMENT OF AN ENTERPRISE TO A COMPONENT BUSINESS MODEL” (hereaftertermed “the above referenced foundation patent application”). CBMprovides a logical and comprehensive view of the enterprise, in termsthat cut across commercial enterprises in general and industries inparticular. Typically, CBM presents business information in the form ofCBM maps at a universal level (cutting across all industries), at anindustry level (cutting across all business within an industry) and atthe level of a particular enterprise within an industry. In principle, amap at a lower level is a subset of, and therefore consistent with, amap at a higher level.

The component business model as described in the above referencedfoundation patent application is based upon a logical partitioning ofbusiness activities into non-overlapping managing concepts, eachmanaging concept being active at the three levels of managementaccountability: providing direction to the business, controlling how thebusiness operates, and executing the operations of the business. Theterm “managing concept” is specially defined as described in the abovereferenced foundation patent application, and is not literally a“managing concept” as that phrase would be understood in the art. Forthe purpose of the present invention, as for the related invention,“managing concept” is the term associated with the following aspects ofthe partitioning methodology. First, the methodology is a partitioningmethodology. The idea is to begin with a whole and partition the wholeinto necessarily non-overlapping parts. Second, experience has shownthat the partitioning process works best when addressed to an asset ofthe business. The asset can be further described by attributes. Third,the managing concept must include mechanisms for doing somethingcommercially useful with the asset. For a sensibly defined managingconcept these mechanisms must cover the full range of managementaccountability levels (i.e. direct, control and execute). Managingconcepts are further partitioned into components, which are cohesivegroups of activities. The boundaries of a component usually fall withina single management accountability level. It is important to emphasizethat the boundaries between managing concepts (and between componentswithin managing concepts) are logical rather than physical.

In order to detect inconsistencies in CBM maps, our approach is torepresent consistency conditions of CBM maps in Web Ontology Language(OWL), and use the OWL inference engine to deduce the potentialinconsistencies of the semantic CBM representation of CBM maps. Thisapproach operates in the following manner:

-   -   The CBM meta-model is represented in OWL.    -   Consistency conditions are represented as further constraints,        which can be OWL expressions or OWL rule expressions, on the        semantic CBM meta-model. The base semantic CBM meta-model and        further constraints form semantic constraints on CBM maps.    -   An OWL inference engine takes the CBM meta-model with        consistency constraints as one input, and instance CBM maps as        another input, then deduces potential inconsistencies between        one CBM map and another CBM map or between CBM maps and the CBM        meta-model after a reasoning process.

OWL is based on a Description Logic (DL). In general, a knowledge baseexpressed in a DL is constituted by two components. The first componentstores a set of universally quantified assertions stating generalproperties of concepts and roles. The second component comprisesassertions on individual objects. Traditionally, the first component iscalled TBox and the second component is called ABox. A typical TBoxassertion states that a concept represents a specialization of anotherconcept. A typical ABox assertion is that a particular object is aninstance of a certain concept.

The central aspect of our approach is to transform CBM inconsistencydetection into a reasoning problem, and use OWL representation toleverage its underlying DL computation capability. In the DL, TBox isused to represent concepts, relationships and their subsumptionhierarchies. ABox in the DL is used to represent instances of conceptsand relationships.

Basically, there are two types of inconsistency problems. The firstissue is consistency among CBM maps, which can be transformed into anABox consistency issue. Suppose we have Retail industry map as an ABoxA1, and enterprise map for Acme is another ABox A2, then we can use theDL inference engine to perform self-consistency validation on A3, whichis the combination of A1 and A2.

Another issue is consistency among CBM maps and the CBM meta-model,which can be transformed into the consistency between TBox and its ABoxinstances. Suppose we have an enterprise map for Acme as an ABox A, andthe CBM meta-model is the TBox T, then the DL inference engine canreason and verify if A comply with the definitions in T in a logicalway.

Our approach has the following advantages:

-   -   OWL provides a sound and complete computational capability,        which can guarantee the results of a consistency check.    -   An ontology is more meaningful and easier to understand for        business people.    -   A semantic model-based consistency check will improve the        correctness of various CBM-based qualitative business analyses,        including the dependency analysis, heat map analysis, and        overlay analysis.

Referring now to the drawings, and more particularly to FIG. 1, there isshown the overall working process of the inconsistency detection andcorresponding information flow and control flow.

Semantic Constraints Generator 115 will take Semantic Model of CBMMeta-model 110 and Inconsistent Conditions of CBM Maps 125 as input, andgenerate a comprehensive set of Semantic Constraints 120 that arerepresented as a mixture of OWL expressions and OWL rule expressions.CBM Maps 130 that are produced by other CBM tools will be imported, andwill be transformed to OWL facts by the OWL Facts Transformer 140, usingthe Semantic Model of CBM Meta-model 110. The result of thistransformation is CBM Maps in OWL 145. Then the OWL Inference Engine 150can take Semantic Constraints 120 and CBM Maps in OWL 145 as input, andverify those constraints on CBM maps by reasoning on the mixture of OWLexpressions, OWL rule expressions and OWL facts. Then OWL InferenceEngine 150 can generate Inconsistency Detection Result 160, which can beconsumed by other tools 170. It should be noted that each of the tools170 may have its own meta-model to describe CBM maps.

FIG. 1A shows a system implementing the invention described in FIG. 1.Semantic Constraints Generator 115 is implemented in Generator computerprogram 115A, OWL Facts Transformer 140 is implemented in Transformercomputer program 140A, and OWL Inference Engine 150 is implemented inInference Engine computer program 150A. Inconsistency Conditions 125,Semantic Model 110, and CBM Maps 130 comprise the input data 105 for thecomputer programs 155 that implement the invention. These inputs aretypically created by other programs (not shown). The computer programs155 produce outputs 135, comprised of Semantic Constraints 120, CBM Mapsin OWL 145 and Inconsistency Detection Result 160. The programs 155,inputs 105, and outputs 135 are stored on server 180, which is connectedto monitor and keyboard assembly 190. Those skilled in the art willrecognize that the implementation shown in FIG. 1A is an exemplarimplementation on a stand-alone device, and that the operativefunctionality of programs 155 can be distributed in a variety ofconfigurations over local area and wide area networks.

The following is a simple example to show how the entire system worksstep by step.

Semantic Model of CBM Meta-model

Here is a small portion of the semantic model of a CBM meta-model, inOWL.

<owl:Class rdf:about=“&emp;BusinessComponent 220”> </owl:Class><owl:Class rdf:about=“&emp;BusinessCompetency 240”> </owl:Class><owl:ObjectProperty rdf:about=“&emp;competency”>   <rdfs:domainrdf:resource=“&emp;BusinessComponent 220”/>   <rdfs:rangerdf:resource=“&emp;BusinessCompetency 240”/> </owl:ObjectProperty><owl:Class rdf:about=“&emp;BusinessService 260”> </owl:Class><owl:ObjectProperty rdf:about=“&emp;usedService”>   <rdfs:domainrdf:resource=“&emp;BusinessComponent 220”/>   <rdfs:rangerdf:resource=“&emp;BusinessService 260”/> </owl:ObjectProperty><owl:Class rdf:about=“&emp;AccountabilityLevel 210”> </owl:Class><owl:ObjectProperty rdf:about=“&emp;accountabilityLevel”>   <rdfs:domainrdf:resource=“&emp;BusinessComponnet 220”/>   <rdfs:rangerdf:resource=“&emp;AccountabilityLevel 210”/> </owl:ObjectProperty>

FIG. 2A shows the graphical view of the above OWL fragment. BusinessComponent 220 has an Accountability Level 210 and has a metric, shown asa key performance indicator (KPI 230). It also has a business competency240. Business Component 220 also has a Business Process 250, which inturn has a Business Activity 270. Business Process 250 is implemented ina Business Service 260.

Returning to FIG. 1, OWL Facts Transformer 140 transforms CBM maps intoOWL facts through mapping between OWL and other modeling languages thatare used to describe CBM maps in other CBM tools 170. For example, Ecoreis the meta-model included within the Eclipse Modeling Framework. IfEcore, rather than OWL, is used to represent the CBM meta-model, thenthe OWL Facts Transformer 140 uses a meta-model mapping between OWL andEcore, as shown in FIG. 2B. Each of meta-model elements shown in FIG. 2Ahas a corresponding element in the Ecore meta-model 200B, as shown bythe double-headed arrows in FIG. 2B: Accountability Level 210 maps toAccountability Level 210B, Business Component 220 maps to BusinessComponent 220B, KPI 230 maps to KPI 230B, Business Competency 240 mapsto Business Competency 240B, Business Process 250 maps to BusinessProcess 250B, Business Service 260 maps to Business Service 260B, andBusiness Activity 270 maps to Business Activity 270B. Note that this isa one-to-one mapping that is operable in both directions. Therefore,given this mapping between Ecore and OWL, the OWL Facts Transformer 140can transform CBM maps represented in Ecore into OWL representations.

Inconsistency Conditions of CBM Map

Suppose there are two simple inconsistency conditions that a CBM mapshould comply with. They cover two usage scenarios; (1) consistencybetween an industry map and an enterprise map, and (2) consistencybetween the CBM maps and the CBM meta-model.

Condition 1: As shown in FIG. 3 and the following OWL fragment, ifbusiness component c 320 has competency p 310 in an industry map, c 320should also have competency p 310 in enterprise map. This is representedby an OWL object property 330.

<owl:ObjectProperty rdf:about=“&emp;hasCompetency”>   <rdf:typerdf:resource= “&owl;FunctionalProperty”/>       <rdfs:domainrdf:resource=“&emp;BusinessComponnet”/>       <rdfs:rangerdf:resource=“&emp;BusinessCompetency”/> </owl:ObjectProperty>

CBM Map in OWL

For the purposes of the following illustrations, we will consider ACMEas an enterprise within the retail industry. As shown in FIG. 4 and thefollowing OWL fragment, the Retail industry map will show that thebusiness component “Demand_Forecast_and_Analysis” 410 has the Marketingbusiness competency 420, the Marketing business competency 420 isdifferent from (owl:differentFrom 425) the FinancialManagement businesscompetency 430.

<BusinessComponent rdf:ID=“&emp;Demand_Forecast_and_Analysis”><hasCompetency>   <BusinessCompetency rdf:ID=“&emp;Marketing”>   <owl:differentFrom>     <BusinessCompetencyrdf:ID=“&emp;FinancialManagement”>      <owl:differentFromrdf:resource=“&emp;Marketing”/>     </BusinessCompetency>   </owl:differentFrom>   </BusinessCompetency>  </hasCompetency></BusinessComponent>

As shown in FIG. 5 and the following OWL fragment, however, the Acmeenterprise map shows that the business component“Demand_Forecast_and_Analysis” 510 has the competencyFinancialManagement 520.

<BusinessComponent rdf:ID=“&emp;Demand_Forecast_and_Analysis”>  <hasCompetency>    <BusinessCompetency rdf:ID=“&emp;FinancialManagement ”/>   </hasCompetency>   </BusinessComponent>

OWL Inference Engine

The DL inference engine takes both the retail industry map and ACMEenterprise map as input to populate its ABox instances. BecausehasCompetency is a functional property, the DL reasoner can deduce thatthe FiancialManagement competency 520 is the same as (owl:sameAs) theMarketing competency 420. At the same time, these two competencies aredeclared as different from each other (owl:differentFrom 425). Thisgenerates a logic conflict under DL model theory.

Inconsistency Detection Result

The DL inference engine can tell that there is an inconsistency betweenthe industry map (in this example, the Retail industry map) and theenterprise map (in this example, the ACME enterprise map) on thedefinition of the demand forecast and analysis component. Then a CBMtool (170 in FIG. 1) can read the detection result and take properactions to remind the consultant.

Condition 2: As shown in FIG. 6 and in the OWL fragment below, abusiness component c 630 has one and only one accountability level. Thisis represented in OWL as a maximum cardinality constraint 610 of one anda minimum cardinality constraint 620 of one.

<owl:Class rdf:about=“&emp;BusinessComponnet”>   <rdfs:subClassOfrdf:resource=“&emp; AcctMaxRes ”/>   <rdfs:subClassOfrdf:resource=“&emp; AcctMinRes ”/> </owl:Class> <owl:Restrictionrdf:about=“&emp; AcctMaxRes ”>   <owl:onPropertyrdf:resource=“&emp;hasAcctLevel”/>  <owl:maxCardinality>1</owl:maxCardinality> </owl:Restriction><owl:Restriction rdf:about=“&emp; AcctMinRes ”>   <owl:onPropertyrdf:resource=“&emp;hasAcctLevel”/>  <owl:minCardinality>1</owl:minCardinality> </owl:Restriction>

Returning to FIG. 1, the inconsistency conditions of CBM Maps 125 definethe conditions that CBM maps should comply with, which will be tested byOWL Inference Engine 150. Each inconsistency condition can be decomposedas conjunctions or disjunctions of simple conditions. By defining amapping between simple conditions and OWL restrictions, SemanticConstraints Generator 115 can transform inconsistency conditions 125into OWL expressions.

For example, a condition “a business component should have one and onlyone accountability level” is a conjunction of two simple conditions: “abusiness component has at least one accountability level” and “abusiness component has one accountability level at most”; these simpleconditions can be transformed to minCardinality restriction 620 andmaxCardinality restrictions 610 in OWL, as shown in FIG. 6.

While the invention has been described in terms of a single preferredembodiment, those skilled in the art will recognize that the inventioncan be practiced with modification within the spirit and scope of theappended claims.

1. A method for automating the validation of consistency of componentbusiness models, comprising: generating semantic constraints from i) asemantic model of a component business model meta-model and ii)inconsistency conditions applicable to at least one component businessmodel (CBM) maps; using said semantic model to transform said at leastone CBM maps into corresponding semantic representations; and applyingsaid semantic constraints to said semantic representations to determineinconsistencies between said component business model meta-model and oneof said CBM maps.
 2. The method of claim 1, wherein said componentbusiness model is comprised of non-overlapping components arranged byaccountability level within non-overlapping managing concepts, whereinone of said CBM maps is a map of a business enterprise, and wherein saidbusiness enterprise operates in an industry and one of said CBM maps isa map of said industry.
 3. The method of claim 2, further comprisingapplying said semantic constraints to said semantic representations todetermine inconsistencies between said enterprise CBM map and saidindustry CBM map.
 4. The method of claim 1, further comprising using aCBM tool to modify said CBM enterprise map to remove saidinconsistencies.
 5. The method of claim 1, wherein Web Ontology Language(OWL) is used to express said semantic model and said semanticrepresentations.
 6. The method of claim 1, wherein said semanticconstraints are represented as a mixture of expressions and ruleexpressions in a semantic markup language.
 7. The method of claim 6,wherein said semantic markup language is Resource Description Framework(RDF).
 8. A system for automating the validation of consistency ofcomponent business models, comprising: a component business modelrepresentation of a business enterprise, further comprising a componentbusiness model meta-model and at least one component business model(CBM) maps of non-overlapping components arranged by accountabilitylevel within non-overlapping managing concepts, one of said at least oneCBM maps being a map of said business enterprise; a generator forgenerating semantic constraints from i) a semantic model of saidcomponent business model meta-model and ii) inconsistency conditionsapplicable to said at least one CBM maps; a transformer for using saidsemantic model to transform said CBM maps into corresponding semanticrepresentations; and an inference engine for applying said semanticconstraints to said semantic representations to determineinconsistencies between said CBM meta-model and one of said CBM maps. 9.The system of claim 8, wherein said business enterprise operates in anindustry and one of said CBM maps is a map of said industry, and whereinsaid inference engine determines inconsistencies between said enterpriseCBM map and said industry CBM map.
 10. The system of claim 9, whereinsaid semantic representation of the industry CBM map is a first ABox andsaid semantic representation of the enterprise CBM map is a second ABox,and the inference engine determines inconsistencies by performing aself-consistency validation on a third ABox, said third ABox being acombination of said first ABox and said second Abox.
 11. The system ofclaim 9, wherein the component business model meta-model is a TBox, andthe inference engine determines inconsistencies by verifying whethersaid first ABox complies with said TBox and whether said second ABoxcomplies with said TBox.
 12. The system of claim 8, further comprising aCBM tool for using said inconsistencies to modify said one of said CBMmaps to remove said inconsistencies.
 13. The system of claim 13, whereinone of said CBM maps is a universal CBM map, and wherein said semanticrepresentation of said universal CBM map is a fourth ABox A4.
 14. Thesystem of claim 19, wherein said inference engine determinesinconsistencies between said enterprise map and said industry map andsaid universal map by performing a self-consistency validation of afifth ABox AS, said fifth ABox being the combination A1+A2+A4. 15.Implementing a service for validating consistency of component businessmodels, comprising the method of: generating semantic constraints fromi) a semantic model of a component business model meta-model and ii)inconsistency conditions applicable to at least one component businessmodel (CBM) maps; using said semantic model to transform said at leastone CBM maps into corresponding semantic representations; and applyingsaid semantic constraints to said semantic representations to determineinconsistencies between i) said component business model meta-model andone of said CBM maps or ii) one of said CBM maps and another of said CBMmaps.
 16. A method for implementing a validation service as in claim 15,wherein said component business model is comprised of non-overlappingcomponents arranged by accountability level within non-overlappingmanaging concepts, wherein one of said CBM maps is a map of a businessenterprise, wherein said business enterprise operates in an industry andone of said CBM maps is a map of said industry, and wherein one of saidCBM maps is a universal map from which said industry CBM map is derived.17. A method for implementing a validation service as in claim 15,further comprising using a CBM tool to modify a CBM map to remove saidinconsistencies.
 18. A computer implemented system for validatingconsistency of component business models, comprising: first computercode for generating semantic constraints from i) a semantic model of acomponent business model meta-model and ii) inconsistency conditionsapplicable to at least one component business model (CBM) maps; secondcomputer code for using said semantic model to transform said at leastone CBM maps into corresponding semantic representations; and thirdcomputer code for applying said semantic constraints to said semanticrepresentations to determine inconsistencies between said componentbusiness model meta-model and one of said CBM maps.
 19. A computerimplemented validation system as in claim 18, wherein said businessenterprise operates in an industry and one of said CBM maps in a map ofsaid industry, further comprising fourth computer code for applying saidsemantic constraints to said semantic representations to determineinconsistencies between said enterprise CBM map and said industry CBMmap.
 20. A computer implemented validation system as in claim 18,further comprising fifth computer code for using a CBM tool to modifysaid one of said CBM maps to remove said inconsistencies.